by Keyword: Chronic obstructive lung disease
Ferrer-Lluis, I, Castillo-Escario, Y, Glos, M, Fietze, I, Penzel, T, Jane, R, (2021). Sleep Apnea & Chronic Obstructive Pulmonary Disease: Overlap Syndrome Dynamics in Patients from an Epidemiological Study Conference Proceedings : ... Annual International Conference Of The Ieee Engineering In Medicine And Biology Society. Ieee Engineering In Medicine And Biology Society. Conference 2021, 5574-5577 
Obstructive sleep apnea (OSA) is a sleep disorder in which repetitive upper airway obstructive events occur during sleep. These events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Chronic obstructive pulmonary disease (COPD) is a disorder which induces a persistent inflammation of the lungs. This condition produces hypoventilation, affecting the blood oxygenation, and leads to an increased risk of developing lung cancer and heart disease. In this study, we evaluated how COPD affects the severity and characteristics of OSA in a multivariate demographic database including polysomnographic signals. Results showed SpO2 subtle variations, such as more non-recovered desaturations and increased time below a 90% SpO2 level, which, in the long term, could worsen the risk to suffer cardiovascular and cerebrovascular diseases.Clinical Relevance - COPD increases the OSA risk due to hypoventilation and altered SpO2 behavior. © 2021 IEEE.
JTD Keywords: Chronic obstructive lung disease, Complication, Epidemiologic studies, Epidemiology, Human, Humans, Oxygen saturation, Pulmonary disease, chronic obstructive, Sleep apnea, obstructive, Sleep disordered breathing, Syndrome
Blanco-Almazán, D, Groenendaal, W, Catthoor, F, Jané, R, (2021). Detection of Respiratory Phases to Estimate Breathing Pattern Parameters using Wearable Bioimpendace Conference Proceedings : ... Annual International Conference Of The Ieee Engineering In Medicine And Biology Society. Ieee Engineering In Medicine And Biology Society. Conference 2021, 5508-5511
Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance - This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters. © 2021 IEEE.
JTD Keywords: algorithms, copd, signals, Algorithm, Algorithms, Bioimpedance, Breathing rate, Chronic obstructive lung disease, Electronic device, Human, Humans, Lung, Pulmonary disease, chronic obstructive, Respiratory rate, Wearable electronic devices
